Fingerprint sensing and matching is a reliable and widely used technique for personal identification or verification. In particular, a common approach to fingerprint identification involves scanning a sample fingerprint or an image thereof and storing the image and/or unique characteristics of the fingerprint image. The characteristics of a sample fingerprint may be compared to information for reference fingerprints already in a database to determine proper identification of a person, such as for verification purposes.
A particularly advantageous approach to fingerprint sensing is disclosed in U.S. Pat. No. 5,963,679 to Setlak et al. and assigned to the assignee of the present invention. The fingerprint sensor is an integrated circuit sensor that drives the user's finger with an electric field signal and senses the electric field with an array of electric field sensing pixels on the integrated circuit substrate. Such sensors are used to control access for many different types of electronic devices such as computers, cell phones, personal digital assistants (PDA's), and the like. In particular, fingerprint sensors are used because they may have a small footprint, are relatively easy for a user to use and they provide reasonable authentication capabilities.
U.S. Published Patent Application No. 2005/0089203 also to Setlak, assigned to the assignee of the present invention, and the entire contents of which are incorporated herein by reference. This application discloses an integrated circuit biometric sensor that may sense multiple biometrics of the user, and that is also adapted to either a static placement sensor or a slide finger sensor. The images collected may be used for matching, such as for authentication, or may be used for navigation, for example. The multiple biometrics, in addition to enhancing matching accuracy, may also be used to provide greater resistance to spoofing.
Another significant advance in finger sensing technology is disclosed in U.S. Pat. No. 5,953,441 also to Setlak et al., assigned to the assignee of the present invention, and the entire contents of which are incorporated by reference. This patent discloses a fingerprint sensor including an array of impedance sensing elements for generating signals related to an object positioned adjacent thereto, and a spoof reducing circuit for determining whether or not an impedance of the object positioned adjacent the array of impedance sensing elements corresponds to a live finger to thereby reduce spoofing of the fingerprint sensor by an object other than a live finger. A spoofing may be indicated and/or used to block further processing. The spoof reducing circuit may detect a complex impedance having a phase angle in a range of about 10 to 60 degrees corresponding to a live finger. The fingerprint sensor may include a drive circuit for driving the array of impedance sensing elements, and a synchronous demodulator for synchronously demodulating signals from the array of impedance sensing elements. The spoof reducing circuit may operate the synchronous demodulator at least one predetermined phase rotation angle. The spoof reducing circuit may cooperate with the synchronous demodulator for synchronously demodulating signals at first and second phase angles and generating an amplitude ratio thereof, and may also compare the amplitude ratio to a predetermined threshold.
“Spoof” fingerprints are typically made using natural and artificial materials, such as gelatin, gum, gummy bears, meat products, clay, Play-Doh, auto body filler, resins, metal, etc. that can be used to imitate the ridges and valleys present in a real fingerprint. As it is desirable to be able to acquire a fingerprint image under any skin condition (dry, moist, etc.) some fingerprint sensors employ real-time gain and other adjustments to obtain the best possible images. In doing so, sensors that detect fingerprints using these approaches are sometimes susceptible to attack using spoofs because these systems are capable of imaging widely varying skin conditions (and other materials).
Spoof detection approaches can be broadly classified into hardware and software based approaches. Hardware based approaches typically involve coupling a biometric device to a finger sensor. For example, previous work in the area of spoof detection and reduction may be considered as having used: A.) impedance classification: determining the impedance characteristics of a material over some frequency range; B.) optical dispersion characteristics; C.) thermal measurements; D.) phase setting and signal amplitude; and E.) finger settling detection. In contrast, a software based approach to spoof detection may not involve changes or additions to a finger sensor. A software based approach may involve additional comparisons of finger samples from a user.
Derakhshani et al., Determination of Vitality from a Non-Invasive Biomedical Measurement for Use in Fingerprint Scanners, Pattern Recognition, Vol. 36, No. 2, 2003, discloses a spoof detection method that uses a temporal perspiration approach. Such an approach may result in a dynamic moisture pattern along the ridges of the user's finger. A signal corresponding to this moisture pattern is obtained by locating ridges in a given image. Features are extracted from ridge signals in a pair of images obtained five seconds apart. Those features are used for classification by aid of a neural network.
Antonelli et al., Fake Finger Detection by Skin Distortion Analysis, IEEE Trans. Info. Forensics and Security, Vol. 1, No. 3, 2006, discloses using differences in elasticity between the real and fake fingers for spoof detection. A user places his finger on a finger sensor and rotates it while exerting pressure to exaggerate finger deformation. A video sequence of fingerprint images is acquired while the user is rotating the finger. Distortion is estimated by calculating a non-rigid motion field between each pair of consecutive fingerprint images. Distortion information is grouped within a set of concentric rings to form a “distortion code.” The “distortion code” is compared with a reference distortion code (for a real finger) to classify the given finger as real or fake. The reference distortion code may be either universal or different for each user.
Chen et al., Fingerprint Deformation for Spoof Detection, Biometric Symposium, Cristal City, Va., 2005, also discloses using an elasticity disparity between real and fake fingers. Chen et al. discloses aligning enrollment and authentication images using minutiae, and calculating the relative non-linear distortion using a thin-plate spline (TPS). Deformation features, reduced through principal components analysis (PCA) are used for classification based on a support vector machine (SVM).
Abhyankar et al., Fingerprint Liveness Detection Using Local Ridge Frequencies and Multiresolution Texture Analysis Techniques, Proc. Int. Conf. Image Processing, 2006, discloses adopting statistical features obtained through multi-resolution texture and local-ridge frequency analysis. Classification is performed using a fuzzy c-means classifier. U.S. Pat. No. 7,505,613 to Russo discloses a method of finger spoof detection that is similar to Abhyankar et al., but adds user adaptability.